由于红外图像对比度低、色彩信息匮乏且灰度级动态范围小,基于红外成像的目标跟踪一直是本领域研究的难点和重点.提出了一种融合灰度核直方图和SURF(speeded up robust features)特征的红外目标跟踪算法.在首帧采用灰度核直方图和SURF特征分别描述目标模板,在以后每帧中利用均值漂移算法快速找到局部最优解.考虑到灰度直方图特征信息量少,跟踪误差逐渐累积,采用改进的SURF特征点匹配算法估算当前帧目标尺度和中心位置,及时修正累积误差,避免跟踪窗口漂移且能自适应调整跟踪窗口大小,此外更新目标模板,最终准确跟踪目标.真实场景实验结果表明,本文算法在目标外观发生较大尺度变化、周边具有相似表观物体时能稳定跟踪目标,具有很强的稳健性,且满足实时性要求.%Because of the low contrast, lack of color, and low dynamic range of infrared images, the object tracking based on infrared imaging is rather difficult. An infrared object tracking algorithm is proposed by integrating the gray kernel histogram and SURF ( speeded up robust features) features. An object template is represented by gray kernel histogram and SURF features in the first frame. The Mean Shift algorithm is used to find the suboptimal position rapidly in the next frame. Because the gray histogram contains less information, the tracking error is accumulated. Then, the improved SURF feature matching algorithm is used to estimate the size and center point of the current frame. The cumulative errors are amended to avoid the tracking window drifting gradually away from the object and the size of tracking window can be self-adapted. Finally, the object template is updated. Experimental results on real situations demonstrate that the proposed algorithm can track objects well in real-time ever when the appearance changes and similar apparents are existing around the targets.
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